The Role of Marketing Metrics and Financial Metrics in Guiding Top-level Management

Transcription

1 The Role of Marketing Metrics and Financial Metrics in Guiding Top-level Management Doctoral Colloquium Paper Markus Lamest School of Business, Trinity College Dublin, Supervisor: Dr Mairead Brady School of Business, Trinity College Dublin, Stage of doctoral research: Early, second year of the PhD program Conceptual domain: The extant literature approaches the field of marketing metrics from different conceptual angels such as control theory, institutional theory or the knowledge-based view of the firm (e.g. Ling-yee, 2011). This study is unique in that it approaches the underlying research question from the perspective of the service dominant logic, which regards the flow of information as the primary and most crucial flow within companies (see Lusch, 2007; Vargo, 2011; Vargo & Lusch, 2007). Methodological domain: The methodology applied is Interpretivism and the author adopts the view of a realist. The main method applied is multiple case studies. The study will consist of a two-stage empirical research process, entailing a preceding online-survey as well as the subsequent investigation of multiple case scenarios. The latter will be dominated by openand semi-structured interviews of top-level managers as well as senior managers from the marketing and finance department. Substantive domain: The study makes a substantive contribution to marketing theory in that it identifies best and poor practice of how metrics are selected, collected, and implemented. The researcher expects to find best practice cases that provide evidence of a balance between marketing and financial metrics. The study is unique in that it (a) takes a primary focus on top-level management, (b) a focus on internal data and (c) appreciates existing metrics from two domains.

2 Introduction As result of the digital revolution, the balance of power between customers and organisations has forever changed (CMO-Council, 2011). In order to both understand and provide value to empowered customers, companies are to understand and identify with individuals needs and demands. The data necessary for companies to excel in this endeavour has increased enormously over the past decades in terms of amount (Leeflang, 2011), types (Day, 2011) and frequency (Hopkins & Brokaw, 2011). Sources of data have evolved from the availability of store level data in the 1950s, over the scanning revolution in 1985, the internet revolution in 1995 to the recent rise of social media, brand communities, information website data and the on-going emergence of an increasing number of new data sources (Kumar et al., 2010a; Leeflang, 2011). Weir (2008:811) states that with the vast databases that are available, firms have the potential [emphasis added] to extract all sorts of knowledge(s) and understandings relating to their current and potential customers. In practice, however, firm s underutilize what they know (Rust et al., 2010). For the purpose of this study metrics are seen as a lens through which to approach these data, convert it into information and finally into knowledge that has the potential to guide strategic decision-making at the top-level. Researchers have suggested that metrics can be tools to drive engagement with the customer (Rust et al., 2010), to decrease the misuse of financial metrics that drive short-sighted management practices (Mizik, 2010) and finally to support the establishment of customer centricity (Shah et al., 2006, Wind, 2008). However, when investigating the phenomenon of metrics, existing empirical literature largely focuses on testing formal relationships between single marketing metrics and financial metrics (e.g. Aksoy et al., 2008; Kumar & Shah, 2009; Luo et al., 2010; Morgan et al., 2009a; Ngobo et al., 2011; O Sullivan et al., 2009b; Rao & Bharadwaj, 2008). What is missing is a holistic investigation of a revised set of metrics as drivers of managerial practice. This study treats the following primary question: How and why do marketing metrics and financial metrics guide top-level management? Literature Review The Context of Marketing Metrics The majority of the studies in the area of marketing metrics are driven by the positivist paradigm. Frequently applied approaches such as the four-factor model, calendar portfolio, stock return response models and persistence modelling (Srinivasan & Hanssens, 2009) clearly fall under the logical positivist approach (Deshpande, 1983). One reason for this onesided distribution is that data on individual customers is often proprietary (Srinivasan & Hanssens, 2009) and only available for a small number of case studies (e.g. the IBM case, Kumar et al., 2008). Hanssens et al. (2009:116) note that data linking marketing actions and their impact on firm value are difficult to obtain - thus the paucity of research in this domain. Similarly, a lack of data on individual customers, which is often proprietary, is the reason why customer valuation metrics are underresearched in comparison to non-financial marketing metrics such as customer satisfaction (Srinivasan et al., 2009). Petersen et al. (2009:103) find that perhaps one of the biggest challenges to overcome in this area is the availability of data on customer and firm value. A substantial part of the academic studies on marketing metrics is based on data external to the firm, for example the publicly available American Customer Satisfaction Index (ACSI) (e.g. Ngobo et al., 2011, Luo et al., 2010, O Connell and O Sullivan, 2010, Jacobson and Mizik, 2009, Aksoy et al., 2008). Businesses today, however, are rather concerned with how to use the vast amount of data available to them internally (Rust et al., 2010). 1

3 Most companies investigated are large in terms of total sales or market share (which are selection-criteria of the ACSI), with many studies focussing on publicly available financial data in order to test formal relationships (e.g. between the ACSI and stock market data). In the majority of the cases, these relationships are found to be significant and positive (e.g. Aksoy et al., 2008, Kumar and Shah, 2009, Luo et al., 2010, Morgan et al., 2009a, Ngobo et al., 2011, O Sullivan et al., 2009b, Rao and Bharadwaj, 2008). A large part of the studies is crosssectional, covering B2B and B2C industries, and there is no single industry-focus. The existing literature on marketing metrics frequently focuses on single measures (Pauwels et al., 2009), which according to Gupta and Zeithaml (2006:733) is justified in that, strong correlations are present among the perceptual metrics we focused on. However, a study by Raithel et al. (2011) shows that when investigating the link to financial outcomes, different drivers of satisfaction vary significantly in their impact. Also, the ability to provide a comprehensive set of metrics is shown to contribute significantly to top-level s satisfaction with marketing s ability to measure performance (O Sullivan & Abela, 2007; O Sullivan et al., 2009a). While the majority of the studies investigates firm-level data, most studies that inquire at a managerial level use a single informant approach (e.g. Grafton et al., 2010; Harmancioglu et al., 2010; Morgan et al., 2009a; O Sullivan et al., 2009a), often focusing on the CMO. Many of the existing articles investigate a certain, predefined set of metrics or they focus on a single metric, which in most cases turns out to be customer satisfaction. However, in order to increase top level s engagement with marketing metrics, academics are arguing, to develop a set of measures small enough to be manageable but large enough to be comprehensive (Clark, 1999:711; see also Clark et al. 2006). The Predominant Role of Traditional Financial Metrics in Practice When it comes to top-level decision-making, financial metrics traditionally play a dominant role. The reason is deeply rooted in the concept of shareholder theory, according to which the primary goal of a company is the maximisation of both shareholder wealth and the valuation of each corporation by financial markets (McSweeney, 2009:839). Over the past decades, the use of financial metrics has become common practice as they are established as an integral part of mandatory financial reports (Beyer et al., 2010). Further contributing to the dominant role of financial metrics is the fact that most managers are, at least to some degree, familiar with the underlying principles, so additional training is unnecessary (Danielson & Scott, 2006). As Hanssens et al. (2009:117) state: One of the best things about balance sheets and financial statements is that everybody understands what most of the numbers mean. However, the recent recession has provided evidence on the disadvantages of traditional financial metrics, namely that they are short-sighted (Mizik & Jacobson, 2007; Mizik, 2010) and are often historical, or backward-looking (Sidhu & Roberts, 2008). Traditional financial metrics are therefore not suited as indicators of strategic success factors in general (Kaplan & Norton, 1996) and incapable of driving customer-centric strategies in particular (Shah et al., 2006; Wind, 2008). The severe consequences of the recent recession have led to a heated debate on the suitability of traditional financial metrics (e.g. Ambler, 2010; Danielson et al., 2008; Davidson, 2009; McDonald, 2010). Mizik (2010:609) demands that marketing researchers need to explore and better understand the role of various marketing metrics and the amount of incremental information they provide to traditional accounting performance measures in depicting the health of a firm. The Limited Role of Marketing Metrics in Practice In practice, customer satisfaction and customer loyalty are the only marketing metrics frequently used by CEOs (Arens & Rust, 2011; Bendle et al., 2010). Executives are 2

4 continuing to rely on satisfaction metrics as the ubiquitous mantra for corporate success, in the belief that high levels of satisfaction may lead to increased customer loyalty, intention to purchase, word-of-mouth recommendation, profit, market share, and return on investment (see Bowden, 2009:63). A shortfall of most metrics in use, such as customer satisfaction, is that they relate to past purchase experiences, i.e. that they are backward looking metrics. Petersen et al. (2009:102) stress that while these metrics can show managers why the firm is at its current state, these metrics have been shown to offer little to no predictive ability to future customer behaviour or firm performance. As LaValle et al. (2011:22) recently note, knowing what happened and why it happened are no longer adequate. Organizations need to know what is happening now, what is likely to happen next and what actions should be taken to get the optimal results. Furthermore, Bowden (2009:63) criticizes that the disadvantage of customer satisfaction is that all customers within the customer base are to be treated alike in the pursuit of high levels of satisfaction, while recent literature ( ) points out that even satisfied customers will defect. It is accepted within the marketing metrics literature that there are no silver metrics (Ambler & Roberts, 2008:733). In order to increase top level s engagement with marketing metrics, academics are arguing to develop a set of measures small enough to be manageable but large enough to be comprehensive (Clark, 1999:711; see also Clark et al. 2006). Marketing Metrics and Technology Rust et al. (2010:96) note, never before have companies had such powerful technologies for interacting directly with customers, collecting and mining information about them and tailoring their offerings according. Over 80% of 1700 CMOs surveyed indicate that they are planning to deploy new technologies to grapple with big data (CMO-Council, 2011:26). Only very few studies in the field of marketing metrics, however, investigate technology involvement. In the field of marketing metrics, dashboards are a tool that both documents and drives the effectiveness of marketing activities (Clark et al., 2006a:20; Pauwels et al., 2009). Davenport (2006) analysed a range of companies concerning their use of analytical tools including dashboards. He calls companies that succeed in using technology in order to employ customer-related data, analytics competitors. Nearly all organization in his survey that are identified as aggressive analytics competitors are found to be clear leaders in their fields, and they attribute much of their success to the masterful exploitation of data (Davenport, 2006). Examples are companies like Amazon, American Airlines, Harrah s, all of whom use massive stores of data about their customers in order to make sophisticated forecasts on customer behaviour. In order to set up a marketing dashboard, top-level management generally has to agree to a set of key marketing metrics. These metrics will then be used to communicate and evaluate the firm s marketing performance and future (Clark et al., 2006a). In the few studies that even include dashboards, O Sullivan and colleagues (2010, 2009, 2007) find that as potential moderators, the use of dashboards is not related to the impact of marketing performance measurement ability on marketing s stature within the firm or firm performance, but practitioners speak a different language. To the question Where are datadriven managers headed? 3,000 executives, managers and analysts answered that the ability to visualize data differently is expected as being the most valuable technique in two years (LaValle et al., 2011:27). O Sullivan and colleagues (2010, 2009, 2007) concede that their finding is likely the result of the simplified conceptualisation of dashboards in their studies. The handful of academic papers (Pauwels et al., 2009:2) in this area do not address how the metrics visualised in dashboards are selected or should be selected in order to guide top-level management. 3

5 Contributions Resulting of the literature review, the researcher has identified four gaps that will be addressed in this PhD study and that are illustrated in figure 1. Figure 1: Conceptual Model and Gaps in the Literature First, while the majority of existing studies are based on external data sources, this study will make a contribution by focussing on data internal to the firm. Second, instead of only investigating a predefined set of (single) metrics, the study will determine and consider the range of metrics that is currently relevant to managerial practice. Third, instead of limiting itself to testing formal relationships, the study will investigate the managerial relevance of metrics, thereby particularly focusing on the role of metrics for top-level managers as those responsible for strategic decision making. A fourth gap is to investigate the role of technology in supporting the visualization and communication of metrics to top-level management, e.g. in the form of dashboards. The extant literature approaches the field of marketing metrics from different conceptual angels such as control theory, institutional theory or the knowledge-based view of the firm (e.g. Ling-yee, 2011). This study is unique in that it approaches the underlying research question from the perspective of the service dominant logic, which regards the flow of information as the primary and most crucial flow within companies (see Lusch, 2007; Vargo, 2011; Vargo & Lusch, 2007). Methodology Guba and Lincoln (1994) distinguish four paradigms to scientific research: Positivism, realism, critical theory and constructivism. While providing a high degree of external validity, statistical generalizability and conceptual replicability (Kim & Richarme, 2010), the existing, positivist studies in the field of marketing metrics do not provide an explanation of how the phenomena under research work in practice. Positivist studies aim at deductive theory testing, while realism, critical theory and constructivism fall under the category of Interpretivism or phenomenology and allow for inductive theory building (Bonoma, 1985; Parkhe, 1993). Also, Interpretivism emphasises the difference between conducting research among people rather than objects (Saunders et al., 2009:116). From the three Interpretivism approaches, the author adopts the realism paradigm as it, compared to critical theory and constructivism, provides the highest degree of researcher objectivity (Healy & Perry, 2000). Realism assumes 4

6 that there is an external reality and that triangulation of data is necessary to refine fallible observations of that reality (Perry, 1998:787) in order to deal with the complexity of these data (Tsoukas, 1989). Methodological Approach: Multiple Case Studies Where research and theory are in their formative stages, case study is regarded as being a valuable approach (Eisenhardt, 1989; Yin, 2009). Case study analysis is defined as a research strategy which focuses on understanding the dynamics present within single settings (Eisenhardt, 1989:534). Another feature of the case study approach is that it is best suited to provide a rich account of interfunctional communication and processes ( ) and obtain access to potentially sensitive and confidential ( ) information (Inglis, 2008:694). Case Selection and Number of Cases The contribution of this study is to investigate multiple case scenarios of how and why toplevel managers are guided by a set of marketing metrics in comparison to financial metrics. The literature holds that companies that are facing fierce competition are particularly dependent on measures which reflect engagement and focus on the customer in order to survive (Darroch, 2009; Rust et al., 2010). The airline industry is one of the most competitive industries in the world. Giovanni Bisignani, CEO of the International Air Transport Association (IATA) states that after a decade of change ( ) profitability is still pathetic (IATA, 2011:6). On the one hand, airlines are generally large, publicly listed companies in which financial metrics may traditionally dominate top-level decision-making. On the other hand, huge competition more than ever forces airlines to focus on customer s needs. Only in 2010, the IATA launched an online airline customer satisfaction benchmarking service, which uses 50 parameters to survey more than 40,000 international passengers and providing partaking airlines with additional customer data (IATA, 2011). In addition, airlines collect and are exposed to a large amount of customer data from frequent fliers, who are responsible for a major part of airline s revenue streams (Harris & Uncles, 2007). Seminal studies in the field of marketing metrics (e.g. Grewal et al., 2010; Rust et al., 2004) are based on data from the airline industry. In case study research, the purpose of analysing multiple cases is not to increase representativeness, but to enhance theory building by either a) producing similar results for predictable reasons (literal replication), or b) producing contrary results for predictable reasons (theoretical replication) (Yin, 2009). This study compares best practice cases to cases in which top-level management experiences a lack of guidance through metrics. At this stage, both the number of cases as well as the case companies are yet to be selected, while an initial contact has already been established with the Germany-based airlines Lufthansa and AirBerlin. The research will be based on multiple case studies in the airline industry. Data Collection The study will consist of a two-stage empirical research process. (1) Online-survey: Based on the extant literature, an initial online survey will be launched, to which a number airlines will be invited to participate. The purpose of this survey is the gathering of information relevant to the design of the subsequent case studies. (2) Subsequent case study research: In the second step, multiple case studies will be conducted, investigating top-level managers as well as senior mangers in finance and marketing. Open- and semi-structured interviews will dominate the data collection process. 5

7 References Aksoy, L, Cooil, B., Groening, C., Keiningham, T. L., & Yalcin, A. (2008). The Long-term Stock Market Valuation of Customer Satisfaction. Journal of Marketing, 72(4), Ambler, Tim, & Roberts, J. H. (2008). Assessing Marketing Performance: Don t Settle for a Silver Metric. Journal of Marketing Management, 24(7/8), Arens, Z., & Rust, R. (2011). The Duality of Decisions and the Case for Impulsiveness Metrics. Journal of the Academy of Marketing Science, published, Bendle, N., Farris, P., Pfeifer, P., & Reibstein, D. (2010). Metrics That Matter to Marketing Managers. Marketing Journal of Research and Management, January, Beyer, A., Cohen, D. A., Lys, T. Z., & Walther, B. R. (2010). The Financial Reporting Environment: Review of the Recent Literature. Journal of Accounting and Economics, 50(2-3), Bonoma, T. V. (1985). Case Research in Marketing: Opportunities, Problems, and a Process. Journal of Marketing Research (JMR), 22(2), American Marketing Association. Bowden, J. L.-H. (2009). The Process of Customer Engagement: A Conceptual Framework. Journal of Marketing Theory & Practice, 17(1), CMO-Council. (2011). From Streched to Strengthened - Insights from the Global Chief Marketing Officer Study, accessed on the 12th of October Clark, B H, Abela, A. V., & Ambler, T. (2006). Behind the Wheel. Marketing Management, 15(3), Clark, Bruce H. (1999). Marketing Performance Measures: History and Interrelationships. Journal of Marketing Management, 15(8), Clark, Bruce H, Abela, A. V., & Ambler, T. (2006). An Information Processing Model of Marketing Performance Measurement. Journal of Marketing Theory & Practice, 14(3), Danielson, M G, & Scott, J. A. (2006). The Capital Budgeting Decisions of Small Businesses. Journal of Applied Finance, 16(2), Danielson, Morris G, Heck, J. L., & Shaffer, D. R. (2008). Shareholder Theory - How Opponents and Proponents Both Get It Wrong. Journal of Applied Finance, 18(2), Darroch, J. (2009). Drucker on Marketing: an Interview with Peter Drucker. Journal of the Academy of Marketing Science, 37(1),

8 Davenport, T. H. (2006). Competing on Analytics. Harvard Business Review, 84(1), Day, George S. (2011). Closing the Marketing Capabilities Gap. Journal of Marketing, 75(4), Deshpande, R. (1983). Paradigms Lost : On Theory and Method in Research in Marketing. Journal of Marketing, 47(4), Eisenhardt, K. M. (1989). Building Theories from Case Study Research. The Academy of Management Review, 14(4), Grafton, J., Lillis, A. M., & Widener, S. K. (2010). The Role of Performance Measurement and Evaluation in Building Organizational Capabilities and Performance. Accounting, organizations and society, 35(7), Grewal, R., Chandrashekaran, M., & Citrin, A. V. (2010). Customer Satisfaction Heterogeneity and Shareholder Value. Journal of Marketing Research (JMR), 47(4), Guba, E. G., & Lincoln, Y. S. (1994). Competing Paradigms in Qualitative Research. Handbook of qualitative research, 2, Gupta, S., & Zeithaml, V. (2006). Customer Metrics and their Impact on Financial Performance. Marketing Science, 25(6), Hanssens, Dominique M, Rust, R. T., & Srivastava, R. K. (2009). Marketing Strategy and Wall Street: Nailing Down Marketing s Impact. Journal of Marketing, 73(6), Harmancioglu, N., Grinstein, A., & Goldman, A. (2010). Innovation and Performance Outcomes of Market Information Collection Efforts: The Role of Top Management Team Involvement. International Journal of Research in Marketing, 27(1), Harris, J., & Uncles, M. (2007). Modeling the Repatronage Behavior of Business Airline Travelers. Journal of Service Research, 9(4), Healy, M., & Perry, C. (2000). Comprehensive Criteria to Judge Validity and Reliability of Qualitative Research within the Realism Paradigm. Qualitative Market Research: An International Journal, 3(3), Hopkins, M. S., & Brokaw, L. (2011). Matchmaking With Math: How Analytics Beats Intuition to Win Customers. MIT Sloan Management Review, 52(2), IATA. (2011). Annual Report, pdf, accessed 22nd of November, Inglis, R. M. (2008). Exploring Accounting and Market Orientation: an Interfunctional Case Study. Journal of Marketing Management, 24(7/8), Kaplan, R. S., & Norton, D. P. (1996). Using the Balanced Scorecard as a Strategic Management System. Harvard Business Review, 74(1),

9 Keiningham, T. L., Cooil, B., Andreassen, T. W., & Aksoy, L. (2007). A Longitudinal Examination of Net Promoter and Firm Revenue Growth. Journal of Marketing, 71(3), Kim, J.-W., & Richarme, M. (2010). Linking Marketing Activities to Shareholder Value: Philosophical and Methodological Issues. Journal of Management & Marketing Research, 5, Kumar, V., & Shah, D. (2009). Expanding the Role of Marketing: From Customer Equity to Market Capitalization. Journal of Marketing, 73(6), Kumar, V., Aksoy, L., Donkers, B., Venkatesan, R., Wiesel, T., & Tillmanns, S. (2010). Undervalued or Overvalued Customers: Capturing Total Customer Engagement Value. Journal of Service Research, 13(3), Kumar, V., Venkatesan, R., Bohling, T., & Beckmann, D. (2008). The Power of CLV: Managing Customer Lifetime Value at IBM. Marketing Science, 27(4), LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big Data, Analytics and the Path From Insights to Value. MIT Sloan Management Review, 52(2), Leeflang, P. (2011). Paving the Way for Distinguished Marketing. International Journal of Research in Marketing, 28(2), Ling-yee, L. (2011). Marketing Metrics Usage: Its Predictors and Implications for Customer Relationship Management. Industrial Marketing Management, 40(1), Luo, X., Homburg, C., & Wieseke, J. (2010). Customer Satisfaction, Analyst Stock Recommendations, and Firm Value. Journal of Marketing Research (JMR), 47(6), Lusch, R F. (2007). Marketing s Evolving Identity: Defining our Future. Journal of Public Policy & Marketing, 26(2), Lusch, R F, & Webster Jr, F. E. (2010). Marketing s Responsibility for the Value of the Enterprise. Marketing Science Institute Working Paper Series, Report No , Lusch, R., Vargo, S., & Obrien, M. (2007). Competing Through Service: Insights from Service-dominant Logic. Journal of Retailing, 83(1), McDonald, M. (2010). A Brief Review of Marketing Accuntability and a Research Agenda. Journal of Business & Industrial Marketing, 25(5), McGovern, G. J., Court, D., Quelch, J. A., & Crawford, B. (2004). Bringing Customers into the Boardroom. Harvard Business Review, 82(11), McSweeney, B. (2009). The Roles of Financial Asset Market Failure Denial and the Economic Crisis: Reflections on Accounting and Financial Theories and Practices. Accounting, organizations and society, 34(6-7),

10 Mizik, N, & Jacobson, R. (2007). Myopic Marketing Management: Evidence of the Phenomenon and its long-term performance consequences in the SEO context. Marketing Science, 26(3), Mizik, Natalie. (2010). The Theory and Practice of Myopic Management. Journal of Marketing Research, 47(4), Morgan, N. A., Slotegraaf, R. J., & Vorhies, D. W. (2009). Linking Marketing Capabilities with Profit Growth. International Journal of Research in Marketing, 26(4), Ngobo, P.-V., Casta, J.-F., & Ramond, O. (2011). Is Customer Satisfaction a Relevant Metric for Financial Analysts? Journal of the Academy of Marketing Science, O Sullivan, D., & Abela, A. V. (2007). Marketing Performance Measurement Ability and Firm Performance. Journal of Marketing, 71(2), O Sullivan, D., & Abela, A. V. (2010). Proving Marketing Success Pays Off! Marketing Performance Measurement and its Effects on Marketing s Stature and Firm Success. GfK-Marketing Intelligence Review, 2(2), O Sullivan, D., Abela, A. V., & Hutchinson, M. (2009). Marketing Performance Measurement and Firm Performance: Evidence from the European High-technology Sector. European Journal of Marketing, 43(5/6), O Sullivan, D., Hutchinson, M. C., & O Connell, V. (2009). Empirical Evidence of the Stock Market s (Mis)pricing of Customer Satisfaction. International Journal of Research in Marketing, 26(2), Parkhe, A. (1993). Messy Research, Methodological Predispositions, and Theory Development in International Joint Ventures. Academy of Management Review, 18(2), Pauwels, K., Ambler, T., Clark, B. H., LaPointe, P., Reibstein, D., Skiera, B., Wierenga, B., et al. (2009). Dashboards as a Service: Why, What, How, and What Research Is Needed? Journal of Service Research, 12(2), Perry, C. (1998). Processes of a Case Study Methodology for Postgraduate Research in Marketing. European Journal of Marketing, 32(9/10), Petersen, J. A., McAlister, L., Reibstein, D. J., Winer, R. S., Kumar, V., & Atkinson, G. (2009). Choosing the Right Metrics to Maximize Profitability and Shareholder Value. Journal of Retailing, 85(1), Raithel, S., Sarstedt, M., Scharf, S., & Schwaiger, M. (2011). On the Value Relevance of Customer Satisfaction. Multiple Drivers and Multiple Markets. Journal of the Academy of Marketing Science, Rao, R. K. S., & Bharadwaj, N. (2008). Marketing Initiatives, Expected Cash Flows, and Shareholders Wealth. Journal of Marketing, 72(1),

11 Reichheld, F. F. (2003). The One Number You Need to Grow. Harvard Business Review, 81(12), Rust, R T, Lemon, K. N., & Zeithaml, V. A. (2004). Return on Marketing: Using Customer Equity to Focus Marketing Strategy. The Journal of Marketing, 68(1), Rust, R T, Moorman, C., & Bhalla, G. (2010). Rethinking Marketing. Harvard Business Review, January-Fe, Saunders, M., Lewis, P., & Thornhill, A. (2009). Research Methods for Business Students. Prentice Hall. Shah, D, Rust, R. T., Parasuraman, A., Staelin, R., & Day, G. S. (2006). The Path to Customer Centricity. Journal of Service Research, 9(2), Sidhu, B., & Roberts, J. (2008). The Marketing Accounting Interface - Lessons and Limitations. Journal of Marketing Management, 24(7), Srinivasan, S., & Hanssens, D. M. (2009). Marketing and Firm Value: Metrics, Methods, Findings, and Future Directions. Journal of Marketing Research, 46(3), Tsoukas, H. (1989). The Validity of Idiographic Research Explanations. Academy of Management Review, 14(4), Vargo, S. L. (2011). On Marketing Theory and Service-dominant Logic: Connecting Some Dots. Marketing Theory, 11(1), 3-8. doi: / Vargo, S. L., & Lusch, R. F. (2007). Service-dominant Logic: Continuing the Evolution. Journal of the Academy of Marketing Science, 36(1), Wind, Y. (2008). A Plan to Invent the Marketing we Need Today. MIT Sloan Management Review, 49(4), 21ñ28. Yin, R. K. (2009). Case Study Research: Design and Methods (Vol. 5). Sage Publications, Inc. 10